Introduction:

using Python.DataFrame is a common operation in data analysis, and it can be achieved using various techniques provided by the Pandas library. In this guide, we will explore the different ways to add a column to a Pandas DataFrame. Adding a new column to a DataFramePandas is one of the most widely used Python libraries for data manipulation and analysis. It provides a rich set of functions to manipulate data, and one of its powerful features is the ability to add columns to an existing

, but we will focus on the two most common methods. We will use a sample dataset to demonstrate the techniques.DataFrameThere are different techniques to add a column in Pandas

method() DataFrame.assignMethod 1: Using

The assign() with the added column. Here's an example:DataFrame. This method returns a new DataFrame method allows us to create a new column and add it to the

Output:

Method 2: Using DataFrame.insert() method

The insert() method allows us to add a new column at a specific location in the DataFrame. This method modifies the original DataFrame and returns None. Here's an example:

Output:

Adding a new column to a DataFrame is a common operation in data analysis. Here are some use cases:

1. Adding calculated columns: We can add a new column to a DataFrame by performing some calculations on existing columns. For example, we can add a new column for the sum of two existing columns, or we can apply a function to a column to create a new column.
2. Adding metadata: We can add metadata to a DataFrame by adding a new column. For example, we can add a column for a timestamp indicating when the data was collected, or we can add a column for a user ID to track who collected the data.

Adding a column to a Pandas DataFrame is a common operation in data analysis. We have discussed two common methods to add a column to a DataFrame using the Pandas library. The assign() method allows us to create a new column and add it to the DataFrame, while the insert() method allows us to add a new column at a specific location in the DataFrame. These methods are versatile and can be used in many use cases, including adding calculated columns or metadata to a DataFrame.